Information Retrieval
Performance Evaluation of Object Detection Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Analysis of Pixel-Level Algorithms for Video Surveillance Applications
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation Metrics for Motion Detection and Tracking
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Towards Perceptually Driven Segmentation Evaluation Metrics
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
Performance evaluation of object detection algorithms for video surveillance
IEEE Transactions on Multimedia
Perceptually-weighted evaluation criteria for segmentation masks in video sequences
IEEE Transactions on Image Processing
Urban Vehicle Tracking Using a Combined 3D Model Detector and Classifier
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Multimedia Tools and Applications
Expert Systems with Applications: An International Journal
Proceedings of the International Working Conference on Advanced Visual Interfaces
Filling the gap in quality assessment of video object tracking
Image and Vision Computing
Genetic programming as strategy for learning image descriptor operators
Intelligent Data Analysis
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The majority of visual surveillance algorithms rely on effective and accurate motion detection. However, most evaluation techniques described in literature do not address the complexity and range of the issues which underpin the design of a good evaluation methodology. In this paper, we explore the problems associated with both the optimising the operating point of any motion detection algorithms and the objective performance comparison of competing algorithms. In particular, we develop an object-based approach based on the F-Measure-a single-valued ROC-like measure which enables a straight-forward mechanism for both optimising and comparing motion detection algorithms. Despite the advantages over pixel-based ROC approaches, a number of important issues associated with parameterising the evaluation algorithm need to be addressed. The approach is illustrated by a comparison of three motion detection algorithms including the well-known Stauffer and Grimson algorithm, based on results obtained on two datasets.